import os import time import numpy as np import math import sys from typing import Iterable, Optional import torch from datasets.mixup import Mixup from timm.utils import accuracy, ModelEma import utils from scipy.special import softmax def train_class_batch(model, samples, target, criterion): outputs = model(samples) outputs = torch.permute(outputs, (0, 2, 1)) loss = criterion(outputs, target) return loss, outputs def get_loss_scale_for_deepspeed(model): optimizer = model.optimizer return optimizer.loss_scale if hasattr(optimizer, "loss_scale") else optimizer.cur_scale def train_one_epoch( model: torch.nn.Module, criterion: torch.nn.Module, data_loader: Iterable, optimizer: torch.optim.Optimizer, device: torch.device, epoch: int, loss_scaler, max_norm: float = 0, model_ema: Optional[ModelEma] = None, mixup_fn: Optional[Mixup] = None, log_writer=None, start_steps=None, lr_schedule_values=None, wd_schedule_values=None, num_training_steps_per_epoch=None, update_freq=None, bf16=False, ): model.train(True) metric_logger = utils.MetricLogger(delimiter=" ") metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}')) metric_logger.add_meter('min_lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}')) metric_logger.add_meter('train_acc', utils.SmoothedValue(window_size=1, fmt='{value:.4f}')) header = 'Epoch: [{}]'.format(epoch) print_freq = 10 if loss_scaler is None: model.zero_grad() model.micro_steps = 0 else: optimizer.zero_grad() for data_iter_step, (samples, targets, _, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): step = data_iter_step // update_freq if step >= num_training_steps_per_epoch: continue it = start_steps + step # global training iteration # Update LR & WD for the first acc if lr_schedule_values is not None or wd_schedule_values is not None and data_iter_step % update_freq == 0: for i, param_group in enumerate(optimizer.param_groups): if lr_schedule_values is not None: param_group["lr"] = lr_schedule_values[it] * param_group["lr_scale"] if wd_schedule_values is not None and param_group["weight_decay"] > 0: param_group["weight_decay"] = wd_schedule_values[it] samples = samples.to(device, non_blocking=True) targets = targets.to(device, non_blocking=True) if loss_scaler is None: samples = samples.bfloat16() if bf16 else samples.half() loss, output = train_class_batch( model, samples, targets, criterion) else: with torch.cuda.amp.autocast(): loss, output = train_class_batch( model, samples, targets, criterion) curr_output = torch.permute(output, (0, 2, 1)) preds = torch.argmax(curr_output, dim=-1) acc_cats = torch.mean((preds == targets).to(torch.float), dim=0) # [num_cats, ] acc1 = torch.mean(acc_cats) metric_logger.update(train_acc=acc1) loss_value = loss.item() if not math.isfinite(loss_value): print("Loss is {}, stopping training".format(loss_value)) sys.exit(1) if loss_scaler is None: loss /= update_freq model.backward(loss) model.step() if (data_iter_step + 1) % update_freq == 0: # model.zero_grad() # Deepspeed will call step() & model.zero_grad() automatic if model_ema is not None: model_ema.update(model) grad_norm = None loss_scale_value = get_loss_scale_for_deepspeed(model) else: # this attribute is added by timm on one optimizer (adahessian) is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order loss /= update_freq grad_norm = loss_scaler(loss, optimizer, clip_grad=max_norm, parameters=model.parameters(), create_graph=is_second_order, update_grad=(data_iter_step + 1) % update_freq == 0) if (data_iter_step + 1) % update_freq == 0: optimizer.zero_grad() if model_ema is not None: model_ema.update(model) loss_scale_value = loss_scaler.state_dict()["scale"] torch.cuda.synchronize() class_acc = acc1 metric_logger.update(loss=loss_value) metric_logger.update(class_acc=class_acc) metric_logger.update(loss_scale=loss_scale_value) min_lr = 10. max_lr = 0. for group in optimizer.param_groups: min_lr = min(min_lr, group["lr"]) max_lr = max(max_lr, group["lr"]) metric_logger.update(lr=max_lr) metric_logger.update(min_lr=min_lr) weight_decay_value = None for group in optimizer.param_groups: if group["weight_decay"] > 0: weight_decay_value = group["weight_decay"] metric_logger.update(weight_decay=weight_decay_value) metric_logger.update(grad_norm=grad_norm) if log_writer is not None: log_writer.update(loss=loss_value, head="loss") log_writer.update(class_acc=class_acc, head="loss") log_writer.update(loss_scale=loss_scale_value, head="opt") log_writer.update(lr=max_lr, head="opt") log_writer.update(min_lr=min_lr, head="opt") log_writer.update(weight_decay=weight_decay_value, head="opt") log_writer.update(grad_norm=grad_norm, head="opt") log_writer.set_step() # gather the stats from all processes metric_logger.synchronize_between_processes() print("Averaged stats:", metric_logger) return {k: meter.global_avg for k, meter in metric_logger.meters.items()} @torch.no_grad() def validation_one_epoch(data_loader, model, device, ds=False, bf16=False): criterion = torch.nn.CrossEntropyLoss() metric_logger = utils.MetricLogger(delimiter=" ") header = 'Val:' # switch to evaluation mode model.eval() for batch in metric_logger.log_every(data_loader, 10, header): videos = batch[0] target = batch[1] videos = videos.to(device, non_blocking=True) target = target.to(device, non_blocking=True) # compute output if ds: videos = videos.bfloat16() if bf16 else videos.half() output = model(videos) output = torch.permute(output, (0, 2, 1)) loss = criterion(output, target) else: with torch.cuda.amp.autocast(): output = model(videos) output = torch.permute(output, (0, 2, 1)) loss = criterion(output, target) #acc1, acc5 = accuracy(output, target, topk=(1, 5)) output = torch.permute(output, (0, 2, 1)) preds = torch.argmax(output, dim=-1) acc_cats = torch.mean((preds == target).to(torch.float), dim=0) # [num_cats, ] acc1 = torch.mean(acc_cats) batch_size = videos.shape[0] metric_logger.update(loss=loss.item()) metric_logger.meters['acc1'].update(acc1, n=batch_size) #metric_logger.meters['acc5'].update(acc5.item(), n=batch_size) # gather the stats from all processes metric_logger.synchronize_between_processes() print('* Acc@1 {top1.global_avg:.3f} loss {losses.global_avg:.3f}' .format(top1=metric_logger.acc1, losses=metric_logger.loss)) return {k: meter.global_avg for k, meter in metric_logger.meters.items()} @torch.no_grad() def final_test(data_loader, model, device, file, ds=False, bf16=False): criterion = torch.nn.CrossEntropyLoss() metric_logger = utils.MetricLogger(delimiter=" ") header = 'Test:' # switch to evaluation mode model.eval() final_result = [] for batch in metric_logger.log_every(data_loader, 10, header): videos = batch[0] target = batch[1] ids = batch[2] chunk_nb = batch[3] split_nb = batch[4] videos = videos.to(device, non_blocking=True) target = target.to(device, non_blocking=True) # compute output if ds: videos = videos.bfloat16() if bf16 else videos.half() output = model(videos) output = torch.permute(output, (0, 2, 1)) loss = criterion(output, target) else: with torch.cuda.amp.autocast(): output = model(videos) output = torch.permute(output, (0, 2, 1)) loss = criterion(output, target) for i in range(output.size(0)): string = "{} {} {} {} {}\n".format(ids[i], \ str(output.data[i].float().cpu().numpy().tolist()), \ str(target[i].cpu().numpy()), \ str(chunk_nb[i].cpu().numpy()), \ str(split_nb[i].cpu().numpy())) final_result.append(string) #acc1, acc5 = accuracy(output, target, topk=(1, 5)) output = torch.permute(output, (0, 2, 1)) preds = torch.argmax(output, dim=-1) acc_cats = torch.mean((preds == target).to(torch.float), dim=0) # [num_cats, ] acc1 = torch.mean(acc_cats) batch_size = videos.shape[0] metric_logger.update(loss=loss.item()) metric_logger.meters['acc1'].update(acc1, n=batch_size) #metric_logger.meters['acc5'].update(acc5.item(), n=batch_size) if not os.path.exists(file): os.mknod(file) with open(file, 'w') as f: f.write("{}\n".format(acc1)) for line in final_result: f.write(line) # gather the stats from all processes metric_logger.synchronize_between_processes() print('* Acc@1 {top1.global_avg:.4f} loss {losses.global_avg:.3f}' .format(top1=metric_logger.acc1, losses=metric_logger.loss)) return {k: meter.global_avg for k, meter in metric_logger.meters.items()} def merge(eval_path, num_tasks): dict_feats = {} dict_label = {} dict_pos = {} print("Reading individual output files") for x in range(num_tasks): file = os.path.join(eval_path, str(x) + '.txt') lines = open(file, 'r').readlines()[1:] for line in lines: line = line.strip() name = line.split(' ')[0] label = line.split(']')[-1].split(' ')[1] chunk_nb = line.split(']')[-1].split(' ')[2] split_nb = line.split(']')[-1].split(' ')[3] data = np.fromstring(' '.join(line.split(' ')[1:]).split('[')[1].split(']')[0], dtype=np.float32, sep=',') data = softmax(data) if not name in dict_feats: dict_feats[name] = [] dict_label[name] = 0 dict_pos[name] = [] if chunk_nb + split_nb in dict_pos[name]: continue dict_feats[name].append(data) dict_pos[name].append(chunk_nb + split_nb) dict_label[name] = label print("Computing final results") input_lst = [] print(len(dict_feats)) for i, item in enumerate(dict_feats): input_lst.append([i, item, dict_feats[item], dict_label[item]]) from multiprocessing import Pool p = Pool(64) ans = p.map(compute_video, input_lst) top1 = [x[1] for x in ans] top5 = [x[2] for x in ans] pred = [x[0] for x in ans] label = [x[3] for x in ans] final_top1 ,final_top5 = np.mean(top1), np.mean(top5) return final_top1*100 ,final_top5*100 def compute_video(lst): i, video_id, data, label = lst feat = [x for x in data] feat = np.mean(feat, axis=0) pred = np.argmax(feat) top1 = (int(pred) == int(label)) * 1.0 top5 = (int(label) in np.argsort(-feat)[:5]) * 1.0 return [pred, top1, top5, int(label)]